Abstract: Data glove is a new dimension in the field of virtual reality environments, initially designed
to satisfy the stringent requirements of modern motion capture and animation professionals. In this
study we try to shift the implementation of data glove from motion animation towards signature
verification problem, making use of the offered multiple degrees of freedom for each finger and for the
hand as well. We used an SVD-based technique to extract the feature values of different sensors’
locating on corresponding fingers in the signing process and evaluated the results for writer
authentication. The technique is tested with large number of authentic and forgery signatures using
data gloves with 14, 5 and 4 sensor and shows a significant level of accuracy with 2.46~5.0% of EER.

INTRODUCTION

In early days, human beings were commonly identified by their names. As the human population increased, method of identifying a person became more sophisticated. People needed to be associated with more information such as family’s background, nationality, gender, age and blood group to label each and every human being as the unique person in the world. The problem of personal identification is multiplied when computer comes into the communication channel of two parties. For this reason, more reliable authentication scheme is needed to build up the required trust of communication link. Password, PINs and token are examples of traditional authentication technology. However, these methods have major drawbacks as passwords and PINs tend to be forgotten or shared out whereas token can be easily lost or stolen. Alternatively, biometry offers potential for automatic personal verification and differently from other biometric means it is not based on the possession of anything or the knowledge of some information. People recognition by means of biometrics[1-3] can be split into two main categories: a) Passive or Physiological biometrics such as face recognition, fingerprint, iris or retina, hand geometry, off-line hand signature and DNA (Deoxyribonucleic Acid) analysis.b) Active or Behavioral biometrics such as voice recognition, hand signature and typing behavior. Signature recognition belongs to this last category and according to market share reports[4] it is the second most important within this group, just behind speech recognition and over keystroke, gait, gesture, etc.